Welcome!

This is the companion website for The Data Analyst's Guide to Cause and Effect (Bendixen & Purzycki, 2026).

Sample chapters (ch. 1 and 2) can be downloaded here.

Data used for analysis examples can be downloaded here.

Blurbs

"The Data Analyst's Guide to Cause and Effect offers an excellent, comprehensive, yet accessible introduction to causal inference. With a light-hearted approach, it opens up a new perspective for those accustomed to traditional statistical analysis, shedding light on crucial aspects of data interpretation. From selecting the right controls to estimating causal effects and even tackling advanced topics like missing data and the intricacies of multilevel modeling, this book is an invaluable guide for analysts seeking to move beyond mere correlation."

— Julia Rohrer, University of Leipzig

"This is a clear and readable book with broad coverage of many ideas and methods in causal inference."

— Andrew Gelman, Columbia University

"The Data Analyst's Guide offers a strongly application-focused introduction to causal inference and is an effective tool for getting data analysts into the world of causal inference and immediately into a workable project."

— Nicholas Huntington-Klein, Seattle University

Version

This is version 1.0

Errata

While we have made every effort to ensure the accuracy and currency of both the book and this website, some errors may have inevitably slipped through. In this section, we will document important errors or updates as they are brought to our attention.